code: def label(self, probabilities: List[Tensor] = None, indices=None):
      if probabilities is not None and indices:
          raise RuntimeError(
              "The `probabilities` and `indices` are mutually exclusive, pass only of one them."
          )
      if probabilities is not None and len(probabilities) != 0:
          probabilities = torch.cat(
              [p[0].unsqueeze(0) for p in probabilities], dim=0
          )
          uncertainties = self.heuristic.get_uncertainties(
              probabilities
          )
          indices = np.argsort(uncertainties)
          if self._dataset is not None:
              self._dataset.label(indices[-self.query_size :])
  
imports: import warnings
  from typing import Any, Callable, Dict, List, Optional
  import numpy
  import torch
  from torch import Tensor
  from torch.utils.data import DataLoader, Dataset, random_split
  from flash.core.data.data_module import DataModule
  from flash.core.data.io.input import InputBase
  from flash.core.data.io.input_transform import create_worker_input_transform_processor
  from flash.core.utilities.imports import _BAAL_AVAILABLE, requires
  from flash.core.utilities.stages import RunningStage
label_desc: Use image node_id 11 for calling the ActiveLearningDataModule obj's underlying member method code with example usage: obj.label(probabilities, indices) without return types